Analytics

Systematic analysis of behavioral data from product usage (clickstreams, events, funnels, session flows) to understand user behavior patterns and identify optimization opportunities.

Process

Key Fields

Question it answers	How are users actually behaving? What is the conversion, engagement, and retention rate? Where are friction points in the flow?
Participants & timing	Continuous passive collection from all users · daily monitoring · weekly and monthly analysis cycles
AI compatibility	AI detects anomalies, segments users by behavior patterns, forecasts trends, and recommends next-best actions.
Output	Analytics dashboards, weekly/monthly reports, anomaly alerts, user behavior segmentation, optimization recommendations

Use when

Product has sufficient traffic (100+ daily active users)
Actual behavior needs to be understood, not just what users say
A continuous optimization process is in place
Impact of changes needs to be measured against a baseline

Skip when

Product is too new to have meaningful behavioral data
Analytics infrastructure does not exist and the cost to build it is prohibitive

Common Mistakes

Measuring everything and concluding nothing

Five hundred metrics in dashboards without clarity on which matter. Pick 3-5 key metrics aligned to business goals plus drill-down detail.

Instrumenting incorrectly

"Button clicked" is useless. "Search button clicked (homepage, query length: 3 characters)" is actionable. Name events with the context needed for analysis.

Interpreting correlation as causation

Feature launched and activation increased; does not mean the feature caused the increase. Seasonal factors, marketing campaigns, or bug fixes may explain the change. Validate with an A/B test or cohort comparison.